Using the amazing IKVM OpenNLP's java files can be converted to a .Net assembly (dll).
Thus allowing you to use the latest releases of OpenNLP from C# (or any other .net language).
So far the .Net assembly has succesfully been used for: Splitting, Tokenising, POS Tagging & Chunking. Full parsing has yet to be fully tested.


(Don't forget to unblock any downloaded files)

Add references to these assemblies in your project & use at will (smile)
The OpenNlp manual is at

You will need the models for your language which are currently here

Note: This is still a java in .net clothes, so care has to be taken over some things.
e.g when loading models the inputstreams are java types (referenced from the assemblies above)

string modelpath = "C:\models\"; \\Wherever you've stored your downloaded models modelInpStream = new + "en-sent.bin"); sentenceModel =new; SentenceDetectorME=new;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace NaturalLanguageProcessingCSharp
    public class EntityExtractor
        /// <summary>
        /// Extractor for the entity types available in openNLP.
        /// Copyright 2013, Don Krapohl
        /// This source is free for unlimited distribution and use
        /// TODO:
        ///     try/catch/exception handling
        ///     filestream closure
        ///     model training if desired
        ///     Regex or dictionary entity extraction
        ///     clean up the setting of the Name Finder model path
        /// </summary>
        /// Call syntax:  myList = ExtractEntities(myInText, EntityType.Person);

        private string sentenceModelPath = "c:\\models\\en-sent.bin";   //path to the model for sentence detection
        private string nameFinderModelPath;                              //NameFinder model path for English names
        private string tokenModelPath = "c:\\models\\en-token.bin";     //model path for English tokens
        public enum EntityType
            Date = 0,

        public List<string> ExtractEntities(string inputData, EntityType targetType)
            /*required steps to detect names are:
             * downloaded sentence, token, and name models from
             * 1. Parse the input into sentences
             * 2. Parse the sentences into tokens
             * 3. Find the entity in the tokens


            //------------------Preparation -- Set Name Finder model path based upon entity type-----------------
            switch (targetType)
                case EntityType.Date:
                    nameFinderModelPath = "c:\\models\\en-ner-date.bin";
                case EntityType.Location:
                    nameFinderModelPath = "c:\\models\\en-ner-location.bin";
                case EntityType.Money:
                    nameFinderModelPath = "c:\\models\\en-ner-money.bin";
                case EntityType.Organization:
                    nameFinderModelPath = "c:\\models\\en-ner-organization.bin";
                case EntityType.Person:
                    nameFinderModelPath = "c:\\models\\en-ner-person.bin";
                case EntityType.Time:
                    nameFinderModelPath = "c:\\models\\en-ner-time.bin";

            //----------------- Preparation -- load models into objects-----------------
            //initialize the sentence detector
   sentenceParser = prepareSentenceDetector();

            //initialize person names model
   nameFinder =  prepareNameFinder();

            //initialize the tokenizer--used to break our sentences into words (tokens)
   tokenizer = prepareTokenizer();

            //------------------  Make sentences, then tokens, then get names--------------------------------

            String[] sentences = sentenceParser.sentDetect(inputData) ; //detect the sentences and load into sentence array of strings
            List<string> results = new List<string>();

            foreach (string sentence in sentences)
                //now tokenize the input.
                //"Don Krapohl enjoys warm sunny weather" would tokenize as
                //"Don", "Krapohl", "enjoys", "warm", "sunny", "weather"
                string[] tokens = tokenizer.tokenize(sentence);

                //do the find
      [] foundNames = nameFinder.find(tokens);

                //important:  clear adaptive data in the feature generators or the detection rate will decrease over time.

                results.AddRange(, tokens).AsEnumerable());

            return results;

#region private methods
        private prepareTokenizer()
   tokenInputStream = new;     //load the token model into a stream
   tokenModel = new; //load the token model
            return new;  //create the tokenizer
        private prepareSentenceDetector()
   sentModelStream = new;       //load the sentence model into a stream
   sentModel = new;// load the model
            return new; //create sentence detector
        private prepareNameFinder()
   modelInputStream = new; //load the name model into a stream
   model = new; //load the model
            return new;                   //create the namefinder


Workaround if an invalid format exception occurs when reading en-pos-maxent.bin
The file en-pos-maxent.bin is actually a zip archive.
If you examine the contents of this zip file, it currently has three files (the others seem to only have 2),   tags.tagdict,  &  pos.model
Delete the tags.tagdict from the zipfile so that it only contains & pos.model
Note: Don't actually unzip  en-pos-maxent.bin just delete tags.dagdict, so that  en-pos-maxent.bin remains a Zip archive containing the remaining 2 files.